





'''simple docstring'''







import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


A         =				logging.get_logger(__name__)

A         =				{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

A         =				{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

A         =				{
    '''abeja/gpt-neox-japanese-2.7b''': 20_48,
}

def 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						Tuple							,	lowerCAmelCase__  :						Tuple)      ->    Optional[int]:

     '''simple docstring'''




     with open(_lowercase							,	'r'							,	encoding='utf-8') as f:
          _lowercase				:   Dict	    =				json.loads(f.read())

     _lowercase				:   Dict	    =				collections.OrderedDict()
     _lowercase				:   str	    =				collections.OrderedDict()
     _lowercase				:   Dict	    =				collections.OrderedDict()
     with open(_lowercase							,	'r'							,	encoding='utf-8') as f:
          _lowercase				:   int	    =				f.readlines()
     _lowercase				:   Dict	    =				[[t.rstrip('\n')] if (t == ',' or ',' not in t) else t.rstrip('\n').split(',') for t in token]
     for idx, b in enumerate(_lowercase):
          _lowercase				:   str	    =				b
          _lowercase				:   Any	    =				idx
          for wd in b:
               _lowercase				:   Any	    =				idx

     return vocab, raw_vocab, ids_to_tokens, emoji



class 				__SCREAMING_SNAKE_CASE      (							__snake_case   ):
      '''simple docstring'''



      lowerCAmelCase__							:   Dict					=		VOCAB_FILES_NAMES
      lowerCAmelCase__							:   str					=		PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__							:   Tuple					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__							:   List[Any]					=		['input_ids', 'attention_mask']

      def __init__(				self     :			str ,UpperCamelCase     :			List[Any] ,UpperCamelCase     :			List[str] ,UpperCamelCase     :			str="<|endoftext|>" ,UpperCamelCase     :			int="<|endoftext|>" ,UpperCamelCase     :			Tuple="<|startoftext|>" ,UpperCamelCase     :			Tuple="<|endoftext|>" ,UpperCamelCase     :			Optional[int]=False ,**UpperCamelCase     :			List[Any] ,)		->	List[Any]:
           super().__init__(
               unk_token=UpperCamelCase ,pad_token=UpperCamelCase ,bos_token=UpperCamelCase ,eos_token=UpperCamelCase ,do_clean_text=UpperCamelCase ,**UpperCamelCase ,)
           if not os.path.isfile(UpperCamelCase	):
                raise ValueError(
                    F'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
                    ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'	)
           if not os.path.isfile(UpperCamelCase	):
                raise ValueError(
                    F'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
                    ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'	)
           _lowercase				:   Optional[Any]	    =				do_clean_text
           _lowercase    ,			_lowercase    ,			_lowercase    ,			_lowercase				:   List[Any]	    =				load_vocab_and_emoji(UpperCamelCase ,UpperCamelCase	)
           _lowercase				:   Tuple	    =				SubWordJapaneseTokenizer(
               vocab=self.vocab ,ids_to_tokens=self.ids_to_tokens ,emoji=self.emoji	)

      @property
      def 	_lowerCamelCase		(				self     :			List[Any]	)		->	Any:
           return len(self.raw_vocab	)

      def 	_lowerCamelCase		(				self     :			str	)		->	Any:
           return dict(self.raw_vocab ,**self.added_tokens_encoder	)

      def 	_lowerCamelCase		(				self     :			Tuple ,UpperCamelCase     :			List[Any]	)		->	Optional[int]:
           return self.subword_tokenizer.tokenize(UpperCamelCase ,clean=self.do_clean_text	)

      def 	_lowerCamelCase		(				self     :			Union[str, Any] ,UpperCamelCase     :			List[Any]	)		->	int:
           return self.vocab.get(UpperCamelCase ,self.vocab.get(self.unk_token	)	)

      def 	_lowerCamelCase		(				self     :			List[Any] ,UpperCamelCase     :			Any	)		->	Optional[Any]:
           return self.subword_tokenizer.convert_id_to_token(UpperCamelCase	)

      def 	_lowerCamelCase		(				self     :			Union[str, Any] ,UpperCamelCase     :			str	)		->	List[str]:
           _lowercase				:   Union[str, Any]	    =				''.join(UpperCamelCase	).strip()
           return out_string

      def 	_lowerCamelCase		(				self     :			Dict ,UpperCamelCase     :			Optional[Any]	)		->	Any:
           _lowercase				:   Union[str, Any]	    =				[]
           for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(UpperCamelCase ,add_special_tokens=UpperCamelCase	) + [self.eos_token_id]	)

           if len(UpperCamelCase	) > self.model_max_length:
                _lowercase				:   List[Any]	    =				input_ids[-self.model_max_length :]
           return input_ids



      def 	_lowerCamelCase		(				self     :			Optional[int] ,UpperCamelCase     :			Optional[Any] ,UpperCamelCase     :			Union[str, Any] = None	)		->	str:
           _lowercase				:   int	    =				0
           if os.path.isdir(UpperCamelCase	):
                _lowercase				:   Dict	    =				os.path.join(
                    UpperCamelCase ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
                _lowercase				:   List[Any]	    =				os.path.join(
                    UpperCamelCase ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']	)
           else:
                _lowercase				:   Tuple	    =				(
                    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
                )
                _lowercase				:   Optional[int]	    =				(
                    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
                )
           with open(UpperCamelCase ,'w' ,encoding='utf-8'	) as writer:
                for token_index, token in self.ids_to_tokens.items():
                     if index != token_index:
                          logger.warning(
                              F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                              ' Please check that the vocabulary is not corrupted!'	)
                          _lowercase				:   Optional[Any]	    =				token_index
                     writer.write(','.join(UpperCamelCase	) + '\n'	)
                     index += 1
           with open(UpperCamelCase ,'w' ,encoding='utf-8'	) as writer:
                json.dump(self.emoji ,UpperCamelCase	)
           return vocab_file, emoji_file



class 				__SCREAMING_SNAKE_CASE      (							__snake_case   ):
      '''simple docstring'''



      def __init__(				self     :			Union[str, Any] ,UpperCamelCase     :			Any ,UpperCamelCase     :			int ,UpperCamelCase     :			Any	)		->	Union[str, Any]:
           _lowercase				:   int	    =				vocab  # same as swe
           _lowercase				:   Union[str, Any]	    =				ids_to_tokens  # same as bpe
           _lowercase				:   List[str]	    =				emoji
           _lowercase				:   List[Any]	    =				np.max([len(UpperCamelCase	) for w in self.vocab.keys()]	)
           _lowercase				:   Union[str, Any]	    =				re.compile(R'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'	)
           _lowercase				:   List[str]	    =				re.compile(R'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'	)
           _lowercase				:   int	    =				re.compile(R'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'	)
           _lowercase				:   List[str]	    =				re.compile(
               R'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'	)
           _lowercase				:   Tuple	    =				re.compile(
               R'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'	)
           _lowercase				:   str	    =				re.compile(
               R'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'	)
           _lowercase				:   Any	    =				'─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
           _lowercase				:   Optional[Any]	    =				'▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
           _lowercase				:   int	    =				str.maketrans({k: '<BLOCK>' for k in keisen + blocks}	)

      def __len__(				self     :			Any	)		->	Any:
           return len(self.ids_to_tokens	)

      def 	_lowerCamelCase		(				self     :			str ,UpperCamelCase     :			Dict	)		->	Optional[int]:
           _lowercase				:   Dict	    =				self.content_repattera.sub('<URL>' ,UpperCamelCase	)
           _lowercase				:   Tuple	    =				self.content_repattera.sub('<EMAIL>' ,UpperCamelCase	)
           _lowercase				:   Any	    =				self.content_repattera.sub('<TEL>' ,UpperCamelCase	)
           _lowercase				:   Optional[Any]	    =				self.content_repattera.sub('<DATE>' ,UpperCamelCase	)
           _lowercase				:   Dict	    =				self.content_repattera.sub('<DATE>' ,UpperCamelCase	)
           _lowercase				:   str	    =				self.content_repattera.sub('<PRICE>' ,UpperCamelCase	)
           _lowercase				:   Optional[Any]	    =				content.translate(self.content_transa	)
           while "<BLOCK><BLOCK>" in content:
                _lowercase				:   Tuple	    =				content.replace('<BLOCK><BLOCK>' ,'<BLOCK>'	)
           return content

      def 	_lowerCamelCase		(				self     :			Optional[Any] ,UpperCamelCase     :			Dict ,UpperCamelCase     :			Optional[Any]=False	)		->	Tuple:
           _lowercase				:   Dict	    =				text.replace(' ' ,'<SP>'	)
           _lowercase				:   Any	    =				text.replace('　' ,'<SP>'	)
           _lowercase				:   Optional[Any]	    =				text.replace('\r\n' ,'<BR>'	)
           _lowercase				:   List[Any]	    =				text.replace('\n' ,'<BR>'	)
           _lowercase				:   List[Any]	    =				text.replace('\r' ,'<BR>'	)
           _lowercase				:   List[str]	    =				text.replace('\t' ,'<TAB>'	)
           _lowercase				:   List[Any]	    =				text.replace('—' ,'ー'	)
           _lowercase				:   List[Any]	    =				text.replace('−' ,'ー'	)
           for k, v in self.emoji["emoji"].items():
                if k in text:
                     _lowercase				:   List[Any]	    =				text.replace(UpperCamelCase ,UpperCamelCase	)
           if clean:
                _lowercase				:   Union[str, Any]	    =				self.clean_text(UpperCamelCase	)

           def check_simbol(UpperCamelCase     :			Union[str, Any]	):
                _lowercase				:   Any	    =				x.encode()
                if len(UpperCamelCase	) == 1 and len(UpperCamelCase	) == 2:
                     _lowercase				:   int	    =				(int(e[0]	) << 8) + int(e[1]	)
                     if (
                         (c >= 0xC_2A1 and c <= 0xC_2BF)
                         or (c >= 0xC_780 and c <= 0xC_783)
                         or (c >= 0xC_AB9 and c <= 0xC_BBF)
                         or (c >= 0xC_C80 and c <= 0xC_DA2)
                     ):
                          return True
                return False

           def checkuae(UpperCamelCase     :			Optional[Any]	):
                _lowercase				:   Tuple	    =				x.encode()
                if len(UpperCamelCase	) == 1 and len(UpperCamelCase	) == 3:
                     _lowercase				:   List[Any]	    =				(int(e[0]	) << 16) + (int(e[1]	) << 8) + int(e[2]	)
                     if c >= 0xE28_080 and c <= 0xE2B_07F:
                          return True
                return False

           _lowercase				:   str	    =				0
           _lowercase				:   Optional[Any]	    =				[]
           while pos < len(UpperCamelCase	):
                _lowercase				:   int	    =				min(len(UpperCamelCase	) ,pos + self.maxlen + 1	) if text[pos] == '<' else pos + 3
                _lowercase				:   Optional[int]	    =				[]  # (token_id, token, pos)
                for e in range(UpperCamelCase ,UpperCamelCase ,-1	):
                     _lowercase				:   Optional[int]	    =				text[pos:e]
                     if wd in self.vocab:
                          if wd[0] == "<" and len(UpperCamelCase	) > 2:
                               _lowercase				:   Optional[Any]	    =				[(self.vocab[wd], wd, e)]
                               break
                          else:
                               candidates.append((self.vocab[wd], wd, e)	)
                if len(UpperCamelCase	) > 0:
                     # the smallest token_id is adopted
                     _lowercase    ,			_lowercase    ,			_lowercase				:   str	    =				sorted(UpperCamelCase ,key=lambda UpperCamelCase	: x[0]	)[0]
                     result.append(UpperCamelCase	)
                     _lowercase				:   str	    =				e
                else:
                     _lowercase				:   str	    =				pos + 1
                     _lowercase				:   Union[str, Any]	    =				text[pos:end]
                     if check_simbol(UpperCamelCase	):
                          result.append('<KIGOU>'	)
                     elif checkuae(UpperCamelCase	):
                          result.append('<U2000U2BFF>'	)
                     else:
                          for i in wd.encode('utf-8'	):
                               result.append('<|byte%d|>' % i	)
                     _lowercase				:   Optional[int]	    =				end
           return result



      def 	_lowerCamelCase		(				self     :			str ,UpperCamelCase     :			Tuple ,UpperCamelCase     :			Optional[int]="\n"	)		->	Optional[int]:
           _lowercase				:   Any	    =				[]
           _lowercase				:   Union[str, Any]	    =				[]
           _lowercase				:   Any	    =				self.ids_to_tokens[index][0]
           if word[:6] == "<|byte" and word[-2:] == "|>":
                byte_tokens.append(int(word[6:-2]	)	)
           else:
                if len(UpperCamelCase	) > 0:
                     words.append(bytearray(UpperCamelCase	).decode('utf-8' ,errors='replace'	)	)
                     _lowercase				:   Tuple	    =				[]
                if word[:7] == "<|emoji" and word[-2:] == "|>":
                     words.append(self.emoji['emoji_inv'][word]	)
                elif word == "<SP>":
                     words.append(' '	)
                elif word == "<BR>":
                     words.append(UpperCamelCase	)
                elif word == "<TAB>":
                     words.append('\t'	)
                elif word == "<BLOCK>":
                     words.append('▀'	)
                elif word == "<KIGOU>":
                     words.append('ǀ'	)
                elif word == "<U2000U2BFF>":
                     words.append('‖'	)
                else:
                     words.append(UpperCamelCase	)
           if len(UpperCamelCase	) > 0:
                words.append(bytearray(UpperCamelCase	).decode('utf-8' ,errors='replace'	)	)
           _lowercase				:   Any	    =				''.join(UpperCamelCase	)
           return text



"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class    __a						( __snake_case							):
				lowerCamelCase     :       List[Any]			       ='mobilenet_v2'







				def __init__( self      ,	UpperCAmelCase=3      ,	UpperCAmelCase=224      ,	UpperCAmelCase=1.0      ,	UpperCAmelCase=8      ,	UpperCAmelCase=8      ,	UpperCAmelCase=6      ,	UpperCAmelCase=32      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase="relu6"      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.8      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=0.0_0_1      ,	UpperCAmelCase=255      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(**UpperCAmelCase   )

					if depth_multiplier <= 0:
						raise ValueError('''depth_multiplier must be greater than zero.'''   )

					lowerCAmelCase_      							=			num_channels
					lowerCAmelCase_      							=			image_size
					lowerCAmelCase_      							=			depth_multiplier
					lowerCAmelCase_      							=			depth_divisible_by
					lowerCAmelCase_      							=			min_depth
					lowerCAmelCase_      							=			expand_ratio
					lowerCAmelCase_      							=			output_stride
					lowerCAmelCase_      							=			first_layer_is_expansion
					lowerCAmelCase_      							=			finegrained_output
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			tf_padding
					lowerCAmelCase_      							=			classifier_dropout_prob
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			layer_norm_eps
					lowerCAmelCase_      							=			semantic_loss_ignore_index







class    __a						( __snake_case							):
				lowerCamelCase     :       Optional[int]			       =version.parse('1.11'							)







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.task == "image-classification":
						return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
						return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return 1E-4